Volume 41 Issue 2
Apr.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 95-102
YANG Peihong, XU Yanjun. A Method for Predicting Speed of Vehicles on Expressways Based on a ST-GCAN Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 95-102. doi: 10.3963/j.jssn.1674-4861.2023.02.010
Citation: YANG Peihong, XU Yanjun. A Method for Predicting Speed of Vehicles on Expressways Based on a ST-GCAN Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 95-102. doi: 10.3963/j.jssn.1674-4861.2023.02.010
shu

A Method for Predicting Speed of Vehicles on Expressways Based on a ST-GCAN Model

doi: 10.3963/j.jssn.1674-4861.2023.02.010
  • 1.

    Qinghai Expressway Operation Management Co., Ltd, Xining 810000, China

  • 2.

    Department of Transportation Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

  • Received Date: 2022-02-28
    Available Online: 2023-06-19
    Abstract
  • The speed of vehicles on expressways is a significant indicator for describing the effectiveness and safety of road transportation system. Accurate prediction of vehicle speed on expressways can contribute to reduction of traffic accidents and improvement of the level of services. In this sense, a prediction method for vehicle speed, called ST-GCAN, is developed, which integrates graph convolutional neural network (GCN), long short-term memo-ry network (LSTM) and attention mechanism into one model. Graph convolutional network is used to extract the spatial correlations of complex networks of expressways, long-short term memory network is used to extract the temporal correlations of historical data of vehicle speed, and attention mechanism is used to aggregate and analyze the correlation between historical data and predicted vehicle speed. In addition, the model employs dense connec-tions and layer normalization to ensure the integrity of information in the prediction model and to solve the problem of covariate shift during training. The model is tested with a dataset of vehicle speed on expressways of the City of Xining, Province of Qinghai, which contains a total of 94 777 hourly observations on 49 road sections at 8 toll sta-tions from May 1 to August 31, 2020. The ST-GCAN model predicts the vehicle speed in the next hour withthe mean absolute error (MAE) of 12.762%, root mean square error (RMSE) of 21.535%, and R2 of 0.651.Compared to the HA model and the ARIMA model, the MAE of the ST-GCAN model is reduced by 11.1% and 19.7%, respec-tively. Compared to other deep learning models, it is reduced by approximately 8.0% to 10%. In conclusion, the ST-GCAN model can accurately estimate vehicle speed on expressways and shall be able to meet the requirements of intelligent traffic control systems.

     

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